Crossref Citations
This Book has been
cited by the following publications. This list is generated based on data provided by Crossref.
Loss, Theresa
Colbrook, Matthew J.
and
Hansen, Anders C.
2022.
Stratified Sampling Based Compressed Sensing for Structured Signals.
IEEE Transactions on Signal Processing,
Vol. 70,
Issue. ,
p.
3530.
Berk, Aaron
Brugiapaglia, Simone
Joshi, Babhru
Plan, Yaniv
Scott, Matthew
and
Yilmaz, Özgür
2022.
A Coherence Parameter Characterizing Generative Compressed Sensing With Fourier Measurements.
IEEE Journal on Selected Areas in Information Theory,
Vol. 3,
Issue. 3,
p.
502.
Adcock, Ben
Brugiapaglia, Simone
and
King–Roskamp, Matthew
2022.
Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing.
Foundations of Computational Mathematics,
Vol. 22,
Issue. 1,
p.
99.
Adcock, Ben
Cardenas, Juan M.
Dexter, Nick
and
Moraga, Sebastian
2022.
High-Dimensional Optimization and Probability.
Vol. 191,
Issue. ,
p.
9.
2022.
Principles of Electron Optics, Volume 4.
p.
2489.
Miyazaki, Jun
Ishikawa, Yuya
and
Kondo, Ryosuke
2022.
Multiwavelength Photothermal Imaging of Individual Single-Walled Carbon Nanotubes Suspended in a Solvent.
The Journal of Physical Chemistry A,
Vol. 126,
Issue. 32,
p.
5483.
Colbrook, Matthew J.
Antun, Vegard
and
Hansen, Anders C.
2022.
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem.
Proceedings of the National Academy of Sciences,
Vol. 119,
Issue. 12,
Parhi, Rahul
and
Unser, Michael
2023.
The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems.
IEEE Signal Processing Letters,
Vol. 30,
Issue. ,
p.
568.
Thesing, Laura
and
Hansen, Anders C.
2023.
Which neural networks can be computed by an algorithm? – Generalised hardness of approximation meets Deep Learning.
PAMM,
Vol. 22,
Issue. 1,
Berk, Aaron
Brugiapaglia, Simone
and
Hoheisel, Tim
2023.
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing.
SIAM Journal on Mathematics of Data Science,
Vol. 5,
Issue. 4,
p.
1102.
López, Oscar
and
Yılmaz, Özgür
2023.
Embracing off-the-grid samples.
Sampling Theory, Signal Processing, and Data Analysis,
Vol. 21,
Issue. 2,
Neyra-Nesterenko, Maksym
and
Adcock, Ben
2023.
NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems.
Sampling Theory, Signal Processing, and Data Analysis,
Vol. 21,
Issue. 1,
Budd, Jeremy M.
van Gennip, Yves
Latz, Jonas
Parisotto, Simone
and
Schönlieb, Carola-Bibiane
2023.
Joint Reconstruction-Segmentation on Graphs.
SIAM Journal on Imaging Sciences,
Vol. 16,
Issue. 2,
p.
911.
Berk, Aaron
Brugiapaglia, Simone
and
Hoheisel, Tim
2024.
Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off.
SIAM Journal on Optimization,
Vol. 34,
Issue. 3,
p.
2609.
McManus, Alex
Becker, Stephen R.
O'Connor, Daniel
and
Dwork, Nicholas
2024.
Deep Learning with Enforced Data Consistency.
p.
01.
Dwork, Nicholas
Englund, Erin K.
and
Barker, Alex J.
2024.
Faster Scanning with Parallel MRI Using a Non-Rectangular Field-Of-View.
p.
01.
Giacchi, Gianluca
Milani, Bastien
and
Franceschiello, Benedetta
2024.
On the Determination of Lagrange Multipliers for a Weighted LASSO Problem Using Geometric and Convex Analysis Techniques.
Applied Mathematics & Optimization,
Vol. 89,
Issue. 2,
Hoppe, Frederik
Verdun, Claudio Mayrink
Krahmer, Felix
Menzel, Marion I.
and
Rauhut, Holger
2024.
With or Without Replacement? Improving Confidence in Fourier Imaging.
p.
66.
Wang, Weiqi
and
Brugiapaglia, Simone
2024.
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions.
IMA Journal of Numerical Analysis,
Pourya, Mehrsa
Boquet-Pujadas, Aleix
and
Unser, Michael
2024.
A Box-Spline Framework for Inverse Problems With Continuous-Domain Sparsity Constraints.
IEEE Transactions on Computational Imaging,
Vol. 10,
Issue. ,
p.
790.